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Principal Component Analysis - How does PCA work in machine learning?
Answer
Principal Component Analysis (PCA) is a machine learning technique that mainly seeks to conserve as much information as possible while reducing the amount of variables in a dataset. As a result, principal component analysis (PCA) is a powerful method for dimensionality reduction and feature selection in datasets.
